See: Description
| Interface | Description |
|---|---|
| BagBasedCategorizerEnsembleLearner<InputType,CategoryType> |
Interface for a bag-based ensemble learner.
|
| Ensemble<MemberType> |
The
Ensemble interface defines the functionality of an "ensemble"
that is typically created by combining together the result of multiple
learning algorithms. |
| Class | Description |
|---|---|
| AbstractBaggingLearner<InputType,OutputType,MemberType,EnsembleType extends Evaluator<? super InputType,? extends OutputType>> |
Learns an ensemble by randomly sampling with replacement
(duplicates allowed) some percentage of the size of the data (defaults to
100%) on each iteration to train a new ensemble member.
|
| AbstractCategorizerOutOfBagStoppingCriteria<InputType,CategoryType> |
Abstract class for implementing a out-of-bag stopping criteria for a
bagging-based ensemble.
|
| AbstractUnweightedEnsemble<MemberType> |
An abstract implementation of the
Ensemble interface for
unweighted ensembles. |
| AbstractWeightedEnsemble<MemberType> |
An abstract implementation of the
Ensemble interface for ensembles
that have a weight associated with each member. |
| AdaBoost<InputType> |
The
AdaBoost class implements the Adaptive Boosting (AdaBoost)
algorithm formulated by Yoav Freund and Robert Shapire. |
| AdditiveEnsemble<InputType,MemberType extends Evaluator<? super InputType,? extends java.lang.Number>> |
An ensemble of regression functions that determine the result by adding
their outputs together.
|
| AveragingEnsemble<InputType,MemberType extends Evaluator<? super InputType,? extends java.lang.Number>> |
An ensemble for regression functions that averages together the output value
of each ensemble member to get the final output.
|
| BaggingCategorizerLearner<InputType,CategoryType> |
Learns an categorization ensemble by randomly sampling with replacement
(duplicates allowed) some percentage of the size of the data (defaults to
100%) on each iteration to train a new ensemble member.
|
| BaggingCategorizerLearner.OutOfBagErrorStoppingCriteria<InputType,CategoryType> |
Implements a stopping criteria for bagging that uses the out-of-bag
error to determine when to stop learning the ensemble.
|
| BaggingRegressionLearner<InputType> |
Learns an ensemble for regression by randomly sampling with replacement
(duplicates allowed) some percentage of the size of the data (defaults to
100%) on each iteration to train a new ensemble member.
|
| BinaryBaggingLearner<InputType> |
The
BinaryBaggingLearner implements the Bagging learning algorithm. |
| BinaryCategorizerSelector<InputType> |
The
BinaryCategorizerSelector class implements a "weak learner"
meant for use in boosting algorithms that selects the best
BinaryCategorizer from a pre-set list by picking the one with the
best weighted error. |
| CategoryBalancedBaggingLearner<InputType,CategoryType> |
An extension of the basic bagging learner that attempts to sample bags that
have equal numbers of examples from every category.
|
| CategoryBalancedIVotingLearner<InputType,CategoryType> |
An extension of IVoting for dealing with skew problems that makes sure that
there are an equal number of examples from each category in each sample that
an ensemble member is trained on.
|
| IVotingCategorizerLearner<InputType,CategoryType> |
Learns an ensemble in a method similar to bagging except that on each
iteration the bag is built from two parts, each sampled from elements from
disjoint sets.
|
| IVotingCategorizerLearner.OutOfBagErrorStoppingCriteria<InputType,CategoryType> |
Implements a stopping criteria for IVoting that uses the out-of-bag
error to determine when to stop learning the ensemble.
|
| MultiCategoryAdaBoost<InputType,CategoryType> |
An implementation of a multi-class version of the Adaptive Boosting
(AdaBoost) algorithm, known as AdaBoost.M1.
|
| OnlineBaggingCategorizerLearner<InputType,CategoryType,MemberType extends Evaluator<? super InputType,? extends CategoryType>> |
An implementation of an online version of the Bagging algorithm for learning
an ensemble of categorizers.
|
| VotingCategorizerEnsemble<InputType,CategoryType,MemberType extends Evaluator<? super InputType,? extends CategoryType>> |
An ensemble of categorizers that determine the result based on an
equal-weight vote.
|
| WeightedAdditiveEnsemble<InputType,MemberType extends Evaluator<? super InputType,? extends java.lang.Number>> |
An implementation of an ensemble that takes a weighted sum of the values
returned by its members.
|
| WeightedAveragingEnsemble<InputType,MemberType extends Evaluator<? super InputType,? extends java.lang.Number>> |
An implementation of an ensemble that takes the weighted average of its
members.
|
| WeightedBinaryEnsemble<InputType,MemberType extends Evaluator<? super InputType,? extends java.lang.Boolean>> |
The
WeightedBinaryEnsemble class implements an Ensemble of
BinaryCategorizer objects where each categorizer is assigned a
weight and the category is selected by choosing the one with the largest
sum of weights. |
| WeightedVotingCategorizerEnsemble<InputType,CategoryType,MemberType extends Evaluator<? super InputType,? extends CategoryType>> |
An ensemble of categorizers where each ensemble member is evaluated with the
given input to find the category to which its weighted votes are assigned.
|